code: def forward(self, inp, temperature=1.0, hard=False, **kwargs):
      neighborhood, center = self.group_divider(inp)
      logits = self.encoder(neighborhood)  # B G C
      logits = self.dgcnn_1(logits, center)  # B G N
      soft_one_hot = F.gumbel_softmax(
          logits, tau=temperature, dim=2, hard=hard
      )  # B G N
      sampled = torch.einsum(
          "b g n, n c -> b g c", soft_one_hot, self.codebook
      )  # B G C
      feature = self.dgcnn_2(sampled, center)
      coarse, fine = self.decoder(feature)
  
      with torch.no_grad():
          whole_fine = (fine + center.unsqueeze(2)).reshape(
              inp.size(0), -1, 3
          )
          whole_coarse = (coarse + center.unsqueeze(2)).reshape(
              inp.size(0), -1, 3
          )
  
      assert fine.size(2) == self.group_size
      ret = (
          whole_coarse,
          whole_fine,
          coarse,
          fine,
          neighborhood,
          logits,
      )
      return ret
  
imports: import torch.nn
  import torch
  import torch.nn.functional
  from lavis.models.ulip_models.pointbert import misc
label_desc: Use image node_id 4 for calling the DiscreteVAE obj's underlying member method code with example usage: obj.forward(inp, temperature, hard) and returns: ret